# Profiling quantum circuits for their efficient execution on single- and multi-core architectures

arXiv: https://arxiv.org/abs/2407.12640 · HTML: https://mlsanity.com/paper/2407.12640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 93 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers](https://mlsanity.com/paper/1901.11054/) (2019)
- [SupermarQ: A Scalable Quantum Benchmark Suite](https://mlsanity.com/paper/2202.11045/) (2022)
- [Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation](https://mlsanity.com/paper/1903.10963/) (2019)
- [Qubit Mapping and Routing via MaxSAT](https://mlsanity.com/paper/2208.13679/) (2022)
- [Optimization of Quantum Circuit Mapping using Gate Transformation and Commutation](https://mlsanity.com/paper/1907.02686/) (2019)
- [Compiler Design for Distributed Quantum Computing](https://mlsanity.com/paper/2012.09680/) (2020)
- [OpenQL : A Portable Quantum Programming Framework for Quantum Accelerators](https://mlsanity.com/paper/2005.13283/) (2020)
- [Improving Quantum Computation by Optimized Qubit Routing](https://mlsanity.com/paper/2206.01294/) (2022)
- [Modeling Short-Range Microwave Networks to Scale Superconducting Quantum Computation](https://mlsanity.com/paper/2201.08825/) (2022)
- [Characterizing Qubit Traffic of a Quantum Intranet aiming at Modular Quantum Computers](https://mlsanity.com/paper/2209.00126/) (2022)

## Abstract (arXiv)

Application-specific quantum computers offer the most efficient means to tackle problems intractable by classical computers. Realizing these architectures necessitates a deep understanding of quantum circuit properties and their relationship to execution outcomes on quantum devices. Our study aims to perform for the first time a rigorous examination of quantum circuits by introducing graph theory-based metrics extracted from their qubit interaction graph and gate dependency graph alongside conventional parameters describing the circuit itself. This methodology facilitates a comprehensive analysis and clustering of quantum circuits. Furthermore, it uncovers a connection between parameters rooted in both qubit interaction and gate dependency graphs, and the performance metrics for quantum circuit mapping, across a range of established quantum device and mapping configurations. Among the various device configurations, we particularly emphasize modular (i.e., multi-core) quantum computing architectures due to their high potential as a viable solution for quantum device scalability. This thorough analysis will help us to: i) identify key attributes of quantum circuits that affect the quantum circuit mapping performance metrics; ii) predict the performance on a specific chip for similar circuit structures; iii) determine preferable combinations of mapping techniques and hardware setups for specific circuits; and iv) define representative benchmark sets by clustering similarly structured circuits.